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Trust Trackers for Computation Offloading in Edge-Based IoT Networks

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Publication date26/07/2021
Host publicationIEEE International Conference on Computer Communications 2021
PublisherIEEE
Number of pages10
ISBN (electronic)9781665403252
ISBN (print)9781665431316
<mark>Original language</mark>English
EventIEEE INFOCOM 2021 - IEEE Conference on Computer Communications - Vancouver, Canada
Duration: 10/05/202113/05/2021
https://ieeexplore.ieee.org/xpl/conhome/9488422/proceeding

Conference

ConferenceIEEE INFOCOM 2021 - IEEE Conference on Computer Communications
Country/TerritoryCanada
CityVancouver
Period10/05/2113/05/21
Internet address

Conference

ConferenceIEEE INFOCOM 2021 - IEEE Conference on Computer Communications
Country/TerritoryCanada
CityVancouver
Period10/05/2113/05/21
Internet address

Abstract

Wireless Internet of Things (IoT) devices will be deployed to enable applications such as sensing and actuation. These devices are typically resource-constrained and are unable to perform resource-intensive computations. Therefore, these jobs need to be offloaded to resource-rich nodes at the edge of the IoT network for execution. However, the timeliness and correctness of edge nodes may not be trusted (such as during high network load or attack). In this paper, we look at the applicability of trust for successful offloading. Traditionally, trust is computed at the application level, with suitable mechanisms to adjust for factors such as recency. However, these do not work well in IoT networks due to resource constraints. We propose a novel device called Trust Tracker (denoted by Σ) that provides higher-level applications with up-to-date trust information of the resource-rich nodes. We prove impossibility results regarding computation offloading and show that Σ is necessary and sufficient for correct offloading. We show that, Σ cannot be implemented even in a synchronous network and we compute the probability of offloading to a bad node, which we show to be negligible when a majority of nodes are correct. We perform a small-scale deployment to demonstrate our approach.

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©2022 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.